考虑行车能见度状况的车辆队列协作控制

本文考虑了能见度状况影响下的车辆队列协作控制问题.针对车辆行驶中可能出现的3种(正常、低以及超低)能见度状况,分析了其对距离传感器测量输出的影响,并建立了具有切换结构的车辆控制模型.基于平均驻留时间技术以及分段Lyapunov函数方法,在不同能见度状况下,得到了能够保证车辆列队跟踪误差稳定的车辆控制器存在条件以及控制器增益求解方法.通过对车辆控制器增加限制条件,得到了能够保证队列稳定性要求以及实现零稳态距离跟踪误差的车辆协作控制算法.通过MATLAB仿真实验以及Ardunio智能小车实验,验证了本文所提出的算法的有效性以及实用性.     

马尔科夫链通信拓扑下的车辆队列最优能耗控制

针对马尔科夫链通信拓扑下的车辆队列控制问题,综合考虑车辆队列的非线性动力学模型和行驶能耗优化目标,提出一种基于分布式状态观测器的车辆队列能耗优化控制方法.由于在马尔科夫链通信拓扑下,部分车辆获取的邻居车辆信息具有动态切换特性,严重影响了车辆队列控制算法的有效性和稳定性.鉴于此,首先,设计一种用于估计领航车辆状态信息的状态观测器,有效避免通讯拓扑切换对队列控制系统造成的干扰;然后,结合车辆的非线性动力学模型与队列优化目标,构建一种基于指数折扣函数的车辆队列能耗优化框架,将车辆队列的能耗优化问题转化为Riccati方程的求解问题,进而得到车辆队列的最优能耗控制输入,在此基础上,通过构造动态通信拓扑下的李雅普诺夫函数,分析车辆队列控制系统的稳定性条件,即只要每个可能的通信拓扑均需包含一个以领航车辆为根的有向生成树,就可使得该车辆队列控制系统满足稳定性和队列稳定性;最后,通过数值仿真验证所提出控制算法的可行性和有效性.     

传感器测量误差下的非线性车辆队列控制

传感器测量误差对车辆队列的有效控制与稳定性造成了较大的影响;通常情况下,大多研究成果将传感器测量误差设定为分布规律已知的随机数列(如高斯分布,泊松分布等),以便采用特定的数理方法消除误差影响;然而对于控制系统中仅满足有界条件的测量误差,仍需开展进一步的深入研究;针对此类现状,以非线性车辆队列控制为研究对象,综合考虑车载传感器的有界测量误差与车辆之间的有向通信拓扑,设计一种基于滑模的车辆队列控制方法;该方法能有效解决有界传感器测量误差下的车辆队列控制问题;此外,在控制过程中利用预设性能控制(PPC,prescribed performance control)理论,进一步约束车辆队列跟踪误差,确保车辆队列的队列稳定性;最后,通过数值仿真的方式验证本文所提出控制算法的有效性和可行性。     

车队速度滚动时域动态规划及非线性控制

考虑自主车辆队列的节能安全问题，本文提出一种车辆队列协同控制方法，该方法可保证车队低能耗安全行驶.首先，充分考虑道路坡度以及车队异质性建立车队非线性模型，利用基于油耗模型的优化指标构建车队速度优化问题，提出一种滚动时域动态规划算法（Receding horizon dynamic programming，RHDP），获得车队的参考速度.然后，基于非线性车辆模型，运用反步法设计车辆跟踪控制器，并进行车队队列稳定性分析.这种协同控制方法的有效性已通过数值仿真和智能交通实验平台的验证.     

基于事件触发机制的自主车辆队列协同控制

为应对通信过程存在的扰动以及减少自主车辆队列控制中的信息冗余、资源浪费,提出了一种基于事件触发机制的自主车辆队列协同控制算法,保证自主车辆队列的稳定运行。首先,针对有向通信拓扑结构下的自主车辆队列设计基于事件触发机制的协同控制算法,即使存在扰动约束,自主车辆也可以在该算法的控制下有效跟随领航车辆的速度,且与相邻车辆保持期望的安全距离。其次,通过设计Lyapunov函数以及分析Zeno行为,证明算法的有效性和安全性。最后,通过MATLAB仿真验证了控制算法的正确性。     

Data-iteration cooperative adaptive cruise control of mixed heterogeneous vehicle platoons with unknown dynamic characteristics

This paper considers the cooperative adaptive cruise control (CACC) problem of mixed heterogeneous vehicle platoons composed of human-driven and CACC vehicles with unknown dynamic characteristics and an alternative to CACC for platooning of the heterogeneous vehicle platoon is presented. Adaptive dynamic programming is firstly used to learn the dynamic characteristics of acceleration of vehicles from the sampled data. Then the data-iteration optimal CACC controller is computed to ensure that each CACC vehicle can reach a desired inter-vehicle distance and desired common velocity with no prior knowledge of the dynamics of vehicles in the mixed platoon. Moreover, the string stability of the mixed vehicle platoon is derived by establishing some sufficient conditions on the acceleration transfer function of adjacent vehicles. Two simulation experiments of a six-vehicle mixed platoon are used to illustrate the effectiveness of the proposed CACC method.     

混合交通环境车辆队列协同控制

车辆队列在提高驾驶安全性、提升交通流量、改善燃油经济性方面具有巨大潜力,但现有研究多针对完全由智能网联车辆组成的队列,难以适用于现实中的混合交通环境.为此,文章研究了人工驾驶车辆与智能网联车辆的混合队列协同控制方法,在智能网联车辆控制设计中引入后车信息,并分析了其对队列稳定性、跟踪性能、燃油经济性的影响.首先构建了一种...     

Cooperative control and communication of connected vehicles considering packet dropping rate

This paper investigates cooperative control and communication scheduling of a platoon of connected vehicles. Considering the effect of packet dropout and the network access limitation, the vehicular platoon is modelled as a discrete-time switching system. We obtain a set of sufficient conditions (where the rate of different platoon control modes is factored in) for string stability and schedulability of the vehicular platoon. A unified framework of network access scheduling and platoon control is proposed, which can guarantee string stability of the platoon of vehicles and resolve network access conflicts among them. Both numerical simulations and experimental studies with laboratory-scale cars have demonstrated the effectiveness of the presented co-design algorithm.     

Fuel efficiency‐oriented platooning control of connected nonlinear vehicles: A distributed economic MPC approach

This paper considers the fuel efficiency‐oriented platooning control problem of connected vehicles. We present a novel distributed economic model predictive control (EMPC) approach to solve the problem of the vehicle platoon subject to nonlinear dynamics and safety constraints. In order to improve fuel economy of the whole vehicle platoon, the fuel consumption criterion is used to design the distributed EMPC strategy for the platoon. Meanwhile, the car‐tracking performance is exploited to guarantee stability and string stability of the platoon. Then the fuel efficiency control problem of the platoon is formulated as a distributed dual‐layer economic optimal control problem, which is solved in a fashion of receding horizon. It is proved that the proposed strategy guarantees asymptotic stability and predecessor‐follower string stability as well as fuel economy of the whole platoon by minimizing the fuel consumption cost. Finally, the effectiveness of the proposed strategy is highlighted by comparing its performance with that of the traditional distributed MPC strategy in numerical simulations.     

基于深度强化学习的网联车辆队列纵向控制

针对车辆队列中多目标控制优化问题,研究基于强化学习的车辆队列控制方法.控制器输入为队列各车辆状态信息以及车辆间状态误差,输出为基于车辆纵向动力学的期望加速度,实现在V2X通信下的队列单车稳定行驶和队列稳定行驶.根据队列行驶场景以及采用的间距策略、通信拓扑结构等特性,建立队列马尔科夫决策过程(Markov decision process,MDP)模型.同时根据队列多输入-多输出高维样本特性,引入优先经验回放策略,提高算法收敛效率.为贴近实际车辆队列行驶工况,仿真基于PreScan构建多自由度燃油车动力学模型,联合Matlab/ Simulink搭建仿真环境,同时引入噪声对队列控制器中动作网络和评价网络进行训练.仿真结果表明基于强化学习的车辆队列控制燃油消耗更低,且控制器实时性更高,对车辆的控制更为平滑.     

基于PMP算法的HEV能量优化控制策略

针对常用混合动力汽车（Hybrid electric vehicle,HEV）中锂离子电池在功率波动较大时难以满足需求,以及单个驱动周期内HEV燃油能耗大且能量不能很好回收等问题,研究采用锂离子电池和超级电容器混合储能系统（Lithium-ion battery and super-capacitor hybrid energy storage system,Li-SC HESS）与内燃机共同驱动HEV运行.结合比例积分粒子群优化算法（Particle swarm optimization-proportion integration,PSO-PI）控制器和Li-SC HESS内部功率限制管理办法,提出一种改进的基于庞特里亚金极小值原理（Pontryagin's minimum principle,PMP）算法的HEV能量优化控制策略,通过ADVISOR软件建立HEV整车仿真模型,验证该方法的有效性与可行性.仿真结果表明,该能量优化控制策略提高了HEV跟踪整车燃油能耗最小轨迹的实时性,节能减排比改进前提高了1.6%~2%,功率波动时减少了锂离子电池的出力,进而改善了混合储能系统性能,对电动汽车关键技术的后续研究意义重大.     

Hierarchical Cooperative Control of Connected Vehicles: From Heterogeneous Parameters to Heterogeneous Structures

As one of the typical applications of connected vehicles (CVs), the vehicle platoon control technique has been proven to have the advantages of reducing emissions, improving traffic throughout and driving safety. In this paper, a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and structures. By separating neighboring information interaction from local dynamics control, the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer, which helps address the heterogeneity in vehicle parameters and structures. Within the proposed framework, an observer is designed for following vehicles to observe the leading vehicle’s states using neighboring communication, while a tracking controller is designed to track the observed leading vehicle using local feedback control. Closed-loop stability in the absence and presence of communication time delay is analyzed, and the observer is further extended to a finite time convergent one to address string stability under general communication topology. Numerical simulation and field experiment verify the effectiveness of the proposed method.      

Vehicle Platoons Through Ring Coupling

In this paper, a novel strategy for the control of a string of vehicles is designed. The vehicles are coupled in a unidirectional ring at the interaction level: each vehicle is influenced by the position of its immediate forward neighbor; the first vehicle in the platoon is influenced by the position of the last vehicle. Through these interactions a cooperative behavior emerges and a platoon of vehicles moving at a constant velocity with constant inter-vehicle spacings is formed. This contrasts with more traditional control schemes where an independent leader vehicle is followed by the remaining vehicles.      

基于Lyapunov函数方法的时滞车辆纵向跟随控制

应用向量Lyapunov函数方法和比较原理,基于非线性车辆动态耦合模型,研究具有时间滞后的车辆跟随系统的指数稳定性问题,得到了车辆跟随系统的指数稳定性判据．根据滑模控制策略确定了车辆跟随系统的纵向控制规律,基于稳定性准则设计了车辆纵向跟随控制器参数．仿真结果表明,基于该方法设计的车辆纵向跟随控制器能使跟踪误差具有较快的收敛率．     

智能车辆队列横纵向有限时间滑模控制

针对智能车辆队列横纵向控制及误差快速收敛问题,本文提出一种分布式横纵向有限时间滑模控制策略.首先,考虑跟踪误差的连锁反应及横纵向耦合效应,利用投影变换建立车辆队列横纵向误差模型,提出一种车辆队列横纵向控制框架.而后,针对误差快速收敛问题,设计非奇异积分终端滑模面(NITSM)与自适应幂次积分趋近律(APIRL),通过构造Lyapunov函数分析系统的有限时间稳定性与队列稳定性.最后,基于Trucksim/Simulink联合仿真以及实车实验进一步验证了本文方法的有效性.结果表明,本文所提方法能保证队列稳定性,并实现误差快速收敛,规避跟踪误差的连锁反应及车辆横向运动对纵向车间距误差的影响.     

有界扰动下异质车辆队列分布式鲁棒经济预测控制

针对有界扰动下异质车辆队列节能与稳定分布式协同控制问题,提出一种新的分布式鲁棒经济模型预测控制(economic model predictive control, EMPC)策略.首先采用不确定误差模型描述有界扰动下异质车辆队列纵向行驶动态特性,再应用tube思想对系统约束进行紧缩设计,补偿有界扰动对系统造成的不确定性.其次,采用局部车辆行驶能耗模型描述车辆队列分布式经济性能优化的有限时域最优控制问题,并利用传统跟踪性能指标设计附加稳定收缩约束函数.进一步,基于系统收缩原理,建立车辆队列闭环系统关于有界扰动的输入-状态稳定性条件.最后,通过与车辆队列传统分布式鲁棒模型预测控制策略的数值仿真对比结果验证了所提出策略的有效性和优越性.     

约束非线性车辆队列分布式多目标模型预测控制

针对具有状态和控制约束的非线性车辆队列系统多目标控制问题,提出一种分布式多目标模型预测控制(model predictive control, MPC)策略.首先,基于前车-后车单向通信拓扑,建立网联车辆队列非线性纵向巡航模型,应用字典序算法描述分布式多目标MPC问题;然后,通过设计弦稳定与收缩约束,并结合MPC三要素条件,保证车辆队列在经济性能与协同性能最优条件下的稳定性和弦稳定性结果;最后,通过典型工况的仿真结果验证所提出策略的有效性.     

通信中断时的网联车辆协作自适应巡航控制

针对自主巡航车队发生通信中断的情况,提出一种由协作式自适应巡航控制(CACC)模式切换至自适应巡航控制(ACC)模式的方法.当通信中断时,将车辆按照通信能力分组优化处理,并将通信中断车辆由CACC模式切换到ACC模式,且保持原CACC模式下的时间间距不变;为了保证车队安全,结合避碰理论和拉格朗日中值定理,提出一种基于最小安全距离的时间间距切换策略;为了保证两次切换后混合巡航车队(车队中既有CACC车辆也有ACC车辆)的稳定性,结合终值定理和频域分析法设计了混合巡航模式下的CACC-ACC控制器.最后通过仿真与现有方法对比,验证了所提出方法的有效性.     

空气流动阻力下非线性车辆队列最优能耗控制方法

为了优化车辆队列在长距离行驶过程中的能源消耗,对空气流动阻力下车辆队列能耗优化间距策略以及相应的队列控制方法进行了研究;首先根据车辆队列在行驶过程中受到的空气流动阻力,建立基于异构风阻系数的车辆动力学模型;其次,设计基于滑模控制的非线性车辆队列控制方法,使其能够在不同风阻系数下稳定地收敛到期望的车辆队列;在此基础上,构建稳态下车辆队列能量消耗评价模型,并通过优化分析,计算能量消耗最优下的车辆队列期望车间距;最后通过数值仿真的手段验证所提控制方法的有效性与可行性;该结果表明:所设计的控制器能够使整个车辆队列达到期望的控制效果;得到的最优车间距能够使得特定条件下车辆队列稳态能量消耗降低。     

Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles

Hybrid Electric Vehicles (HEVs) generate the power required to drive the vehicle via a combination of internal combustion engines and electric generators. To make HEVs as efficient as possible, proper management of the different energy elements is essential. This task is performed using the HEV control strategy. The HEV control strategy is the algorithm according to which energy is produced, used and saved. This paper describes a genetic-fuzzy control strategy for parallel HEVs. The genetic-fuzzy control strategy is a fuzzy logic controller that is tuned by a genetic algorithm. The objective is to minimize fuel consumption and emissions, while enhancing or maintaining the driving performance characteristics of the vehicle. The tuning process is performed over three different driving cycles including NEDC, FTP and TEH-CAR. Results from the computer simulation demonstrate the effectiveness of this approach in reducing fuel consumption and emissions without sacrificing vehicle performance.